Given an image that consists of a plurality of component regions, its image can be partitioned into zones of influence (ZOI). In the ZOI image, each pixel is associated with a component label. The component (foreground) pixels in the component regions are labeled by their component labels. The non-component (background) pixels are labeled by the labels of the component that is closest to the pixels.
ZOI is a powerful tool to integrate results of multiple images acquired from different aspects of the same objects of interest. In one typical application scenario, the component regions are detected from one image or one detection mode. The component regions are used to form the ZOI. The ZOI is then used to condition the detection or perform measurements of other images or other detection modes. One application example is cell analysis in high content screening, cancer diagnosis, prenatal testing, genetic testing, tissue-based pathology assays, etc. In this application, cell nuclei are stained and imaged in one image for nuclear segmentation. Whole cells or specific cell characteristics are stained and acquired in another image for whole cell or specific cell characteristics segmentation. Since nuclei tend to be isolated from each other and whole cells tend to overlap, it is advantageous to divide the cell regions guided by the nuclear segmentation. ZOI could be used for cell region separation.
The ZOI operation is related to skeletal influence zones which are derived from Voronoi skeletons or medial axes that transform a 2D object into 1D line representation (R. Ogniewicz and M. Ilg, “Voronoi skeletons: Theory and applications,” in IEEE Comp. Vision and Pattern Rec., June 1992, pp. 63-69; Ogniewicz, R. L. and Kubler. O: Hierarchic Voronoi Skeletons, Pattern Recognition, nr. 28, pp. 343-359, 1995). Voronoi skeletons or medial axes are derived from the concept of distance transformation (A. Meijster, J. B. T. M. Roerdink, W. H. Hesselink, “A General Algorithm For Computing Distance Transforms In Linear Time”, 2000; Mihail N. Kolountzakis, Kiriakos N. Kutulakos, “Fast Computation of the Euclidean Distance Map for Binary Images”, Information Processing Letters, 1992). The ZOI operation is also related to recursive morphology (Haralick, R. M., Shapiro, L. G., “Computer and Robot Vision”, Volume I, pp. 236-237, Addison-Wesley, 1992; Serra, J. “Image Analysis and Mathematical Morphology”, PP. 385-387, Academic Press, 1982). The ZOI regions are constructed by recursive dilation and conditioning operations.
The distance transform can be computed sequentially by a two-pass procedure. The method is very fast. It requires O(N2) time for an N by N image. However, the skeletonization requires iterative thinning or similar techniques. The processing time of these methods is shape dependent and sometimes time consuming for complex shapes. The recursive morphology operation is not efficient for a general purpose computer and is time consuming for complex shapes. It is desirable to have a fast and time predictable method to partition regions into zones of influence.
The prior art ZOI method uses the same distance metric for all components. This is useful only for simple applications where all The prior art ZOI method uses the same distance metric for all components. This is useful only for simple applications where all components are not differentiated. This is often not the case in practical applications. For example, in cell analysis applications, different cell types could be contained in the same image. In this case, it is disadvantageous to determine ZOI using the same distance metric across all cell types since large and small cells will be divided equally. It is desirable to have an adaptive ZOI algorithm that could adaptively apply a distance metric dependent on the component characteristics. For example, different distance scales could be used for different cell types or the distance could be scaled according to the size of the component.
The same limitation exists in morphological dilation or erosion operations and their combinations, such as opening and closing. The prior art morphological operations use the same structuring element for all pixels in an image. The structuring element cannot be adaptively changed according to a pixel's component region property. Therefore, when trying to fill holes of a component region, other regions may be merged. Thus, the connectivity (homotopic relations) is destroyed. It is desirable to have an adaptive morphological processing method that adaptively adjusts the structuring element size and shape according to the characteristics of the components.